452 citations · 1k across the 17 of their papers we have counts for
6 papers · 1 filter
UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image Segmentation
Ali Hatamizadeh, Ziyue Xu, Dong Yang +3
Vision Transformers (ViT)s have recently become popular due to their outstanding modeling capabilities, in particular for capturing long-range information, and scalability to datas…
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation
Pengfei Guo, Dong Yang, Ali Hatamizadeh +14
Federated learning (FL) is a distributed machine learning technique that enables collaborative model training while avoiding explicit data sharing. The inherent privacy-preserving…
Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures
Holger R. Roth, Dong Yang, Wenqi Li +5
Building robust deep learning-based models requires diverse training data, ideally from several sources. However, these datasets cannot be combined easily because of patient privac…
Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation
Yingda Xia, Dong Yang, Wenqi Li +15
Federated learning (FL) enables collaborative model training while preserving each participant's privacy, which is particularly beneficial to the medical field. FedAvg is a standar…
Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan
Dong Yang, Ziyue Xu, Wenqi Li +17
The recent outbreak of COVID-19 has led to urgent needs for reliable diagnosis and management of SARS-CoV-2 infection. As a complimentary tool, chest CT has been shown to be able t…
Federated Learning for Breast Density Classification: A Real-World Implementation
Holger R. Roth, Ken Chang, Praveer Singh +40
Building robust deep learning-based models requires large quantities of diverse training data. In this study, we investigate the use of federated learning (FL) to build medical ima…